def make_test_data(organism):
experiment = Experiment()
experiment.accession_code = "GSE51088"
experiment.technology = "RNA-SEQ"
experiment.save()
xoa = ExperimentOrganismAssociation()
xoa.experiment = experiment
xoa.organism = organism
xoa.save()
result = ComputationalResult()
result.save()
sample = Sample()
sample.accession_code = "GSM1237818"
sample.title = "GSM1237818"
sample.organism = organism
sample.technology = "RNA-SEQ"
sample.is_processed = True
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.s3_key = "smasher-test-quant.sf"
computed_file.s3_bucket = "data-refinery-test-assets"
computed_file.filename = "quant.sf"
computed_file.absolute_file_path = "/home/user/data_store/QUANT/smasher-test-quant.sf"
computed_file.result = result
computed_file.is_smashable = True
computed_file.size_in_bytes = 123123
computed_file.sha1 = (
"08c7ea90b66b52f7cd9d9a569717a1f5f3874967" # this matches with the downloaded file
)
computed_file.save()
computed_file = ComputedFile()
computed_file.filename = "logquant.tsv"
computed_file.is_smashable = True
computed_file.size_in_bytes = 123123
computed_file.result = result
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
def _create_result_objects(job_context: Dict) -> Dict:
""" Create the ComputationalResult objects after a Scan run is complete """
result = ComputationalResult()
result.commands.append("SCAN.UPC::SCAN_TwoColor")
result.is_ccdl = True
result.is_public = True
result.time_start = job_context["time_start"]
result.time_end = job_context["time_end"]
try:
processor_key = "AGILENT_TWOCOLOR"
result.processor = utils.find_processor(processor_key)
except Exception as e:
return utils.handle_processor_exception(job_context, processor_key, e)
result.save()
job_context["pipeline"].steps.append(result.id)
# Create a ComputedFile for the sample,
# sync it S3 and save it.
try:
computed_file = ComputedFile()
computed_file.absolute_file_path = job_context["output_file_path"]
computed_file.filename = os.path.split(
job_context["output_file_path"])[-1]
computed_file.calculate_sha1()
computed_file.calculate_size()
computed_file.result = result
computed_file.is_smashable = True
computed_file.is_qc = False
computed_file.save()
job_context["computed_files"].append(computed_file)
except Exception:
logger.exception(
"Exception caught while moving file %s to S3",
computed_file.filename,
processor_job=job_context["job_id"],
)
failure_reason = "Exception caught while moving file to S3"
job_context["job"].failure_reason = failure_reason
job_context["success"] = False
return job_context
for sample in job_context["samples"]:
assoc = SampleResultAssociation()
assoc.sample = sample
assoc.result = result
assoc.save()
SampleComputedFileAssociation.objects.get_or_create(
sample=sample, computed_file=computed_file)
logger.info("Created %s", result)
job_context["success"] = True
return job_context
def test_fail(self):
""" Test our ability to fail """
result = ComputationalResult()
result.save()
sample = Sample()
sample.accession_code = 'XXX'
sample.title = 'XXX'
sample.organism = Organism.get_object_for_name("HOMO_SAPIENS")
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
computed_file = ComputedFile()
computed_file.filename = "NOT_REAL.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['XXX']}
ds.aggregate_by = 'EXPERIMENT'
ds.scale_by = 'MINMAX'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
dsid = ds.id
job = ProcessorJob()
job.pipeline_applied = "SMASHER"
job.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(job.pk, upload=False)
ds = Dataset.objects.get(id=dsid)
print(ds.failure_reason)
print(final_context['dataset'].failure_reason)
self.assertNotEqual(final_context['unsmashable_files'], [])
def _create_result_objects(job_context: Dict) -> Dict:
""" Create the ComputationalResult objects after a Scan run is complete """
result = ComputationalResult()
result.commands.append('SCAN.UPC::SCANfast')
result.is_ccdl = True
result.is_public = True
result.time_start = job_context['time_start']
result.time_end = job_context['time_end']
try:
processor_key = "AFFYMETRIX_SCAN"
result.processor = utils.find_processor(processor_key)
except Exception as e:
return utils.handle_processor_exception(job_context, processor_key, e)
result.save()
job_context['pipeline'].steps.append(result.id)
# Create a ComputedFile for the sample
computed_file = ComputedFile()
computed_file.absolute_file_path = job_context["output_file_path"]
computed_file.filename = os.path.split(job_context["output_file_path"])[-1]
computed_file.calculate_sha1()
computed_file.calculate_size()
computed_file.result = result
computed_file.is_smashable = True
computed_file.is_qc = False
computed_file.save()
job_context['computed_files'].append(computed_file)
for sample in job_context['samples']:
assoc = SampleResultAssociation()
assoc.sample = sample
assoc.result = result
assoc.save()
SampleComputedFileAssociation.objects.get_or_create(
sample=sample, computed_file=computed_file)
logger.debug("Created %s", result, processor_job=job_context["job_id"])
job_context["success"] = True
return job_context
def _create_result(job_context: Dict) -> Dict:
""" Create the actual Result object"""
# This is a NO-OP, but we make a ComputationalResult regardless.
result = ComputationalResult()
result.commands.append(job_context["script_name"])
result.is_ccdl = True
try:
processor_key = "SUBMITTER_PROCESSED"
result.processor = utils.find_processor(processor_key)
except Exception as e:
return utils.handle_processor_exception(job_context, processor_key, e)
result.save()
job_context["pipeline"].steps.append(result.id)
# Create a ComputedFile for the computed file,
# sync it S3 and save it.
computed_file = ComputedFile()
computed_file.absolute_file_path = job_context["output_file_path"]
computed_file.filename = job_context["output_file_path"].split("/")[-1]
computed_file.calculate_sha1()
computed_file.calculate_size()
computed_file.result = result
computed_file.is_smashable = True
computed_file.is_qc = False
computed_file.save()
# utils.end_job will sync this to S3 for us.
job_context["computed_files"] = [computed_file]
for sample in job_context["samples"]:
assoc = SampleResultAssociation()
assoc.sample = sample
assoc.result = result
assoc.save()
SampleComputedFileAssociation.objects.get_or_create(
sample=sample, computed_file=computed_file
)
logger.debug("Created %s", result)
job_context["success"] = True
return job_context
def create_sample_for_experiment(sample_info: Dict,
experiment: Experiment) -> Sample:
result = ComputationalResult()
result.save()
sample = Sample()
sample.accession_code = sample_info["accession_code"]
sample.title = sample_info.get("title",
None) or sample_info["accession_code"]
sample.organism = sample_info["organism"]
sample.technology = sample_info["technology"]
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
if sample_info.get("filename") is not None:
computed_file = ComputedFile()
computed_file.filename = sample_info["filename"]
computed_file.absolute_file_path = sample_info[
"data_dir"] + sample_info["filename"]
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
return sample
def test_no_smash_dupe(self):
""" """
job = ProcessorJob()
job.pipeline_applied = "SMASHER"
job.save()
experiment = Experiment()
experiment.accession_code = "GSE51081"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
sample = Sample()
sample.accession_code = 'GSM1237810'
sample.title = 'GSM1237810'
sample.organism = homo_sapiens
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1237810_T09-1084.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
result = ComputationalResult()
result.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sample = Sample()
sample.accession_code = 'GSM1237811'
sample.title = 'GSM1237811'
sample.organism = homo_sapiens
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
result = ComputationalResult()
result.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['GSM1237810', 'GSM1237811']}
ds.aggregate_by = 'ALL'
ds.scale_by = 'STANDARD'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(job.pk, upload=False)
dsid = ds.id
ds = Dataset.objects.get(id=dsid)
self.assertTrue(ds.success)
for column in final_context['original_merged'].columns:
self.assertTrue('_x' not in column)
def test_create_quantpendia(self):
job = ProcessorJob()
job.pipeline_applied = ProcessorPipeline.CREATE_QUANTPENDIA.value
job.save()
experiment = Experiment()
experiment.accession_code = "GSE51088"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS",
taxonomy_id=9606)
sample = Sample()
sample.accession_code = "GSM1237818"
sample.title = "GSM1237818"
sample.organism = homo_sapiens
sample.technology = "RNA-SEQ"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.s3_key = "smasher-test-quant.sf"
computed_file.s3_bucket = "data-refinery-test-assets"
computed_file.filename = "quant.sf"
computed_file.absolute_file_path = "/home/user/data_store/QUANT/smasher-test-quant.sf"
computed_file.result = result
computed_file.is_smashable = True
computed_file.size_in_bytes = 123123
computed_file.sha1 = (
"08c7ea90b66b52f7cd9d9a569717a1f5f3874967" # this matches with the downloaded file
)
computed_file.save()
computed_file = ComputedFile()
computed_file.filename = "logquant.tsv"
computed_file.is_smashable = True
computed_file.size_in_bytes = 123123
computed_file.result = result
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {"GSE51088": ["GSM1237818"]}
ds.aggregate_by = "EXPERIMENT"
ds.scale_by = "STANDARD"
ds.email_address = "*****@*****.**"
ds.quant_sf_only = True # Make the dataset include quant.sf files only
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = ds
pjda.save()
final_context = create_quantpendia(job.id)
self.assertTrue(
os.path.exists(final_context["output_dir"] +
"/GSE51088/GSM1237818_quant.sf"))
self.assertTrue(
os.path.exists(final_context["output_dir"] + "/README.md"))
self.assertTrue(
os.path.exists(final_context["output_dir"] + "/LICENSE.TXT"))
self.assertTrue(
os.path.exists(final_context["output_dir"] +
"/aggregated_metadata.json"))
self.assertTrue(final_context["metadata"]["quant_sf_only"])
self.assertEqual(final_context["metadata"]["num_samples"], 1)
self.assertEqual(final_context["metadata"]["num_experiments"], 1)
# test that archive exists
quantpendia_file = ComputedFile.objects.filter(
is_compendia=True, quant_sf_only=True).latest()
self.assertTrue(os.path.exists(quantpendia_file.absolute_file_path))
def test_no_smash_all_diff_species(self):
""" Smashing together with 'ALL' with different species is a really weird behavior.
This test isn't really testing a normal case, just make sure that it's marking the
unsmashable files.
"""
job = ProcessorJob()
job.pipeline_applied = "SMASHER"
job.save()
experiment = Experiment()
experiment.accession_code = "GSE51081"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
sample = Sample()
sample.accession_code = 'GSM1237810'
sample.title = 'GSM1237810'
sample.organism = homo_sapiens
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1237810_T09-1084.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
result = ComputationalResult()
result.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
experiment = Experiment()
experiment.accession_code = "GSE51084"
experiment.save()
mus_mus = Organism.get_object_for_name("MUS_MUSCULUS")
sample = Sample()
sample.accession_code = 'GSM1238108'
sample.title = 'GSM1238108'
sample.organism = homo_sapiens
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1238108-tbl-1.txt"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['GSM1237810'], 'GSE51084': ['GSM1238108']}
ds.aggregate_by = 'ALL'
ds.scale_by = 'STANDARD'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(job.pk, upload=False)
dsid = ds.id
ds = Dataset.objects.get(id=dsid)
print(ds.failure_reason)
print(final_context['dataset'].failure_reason)
self.assertEqual(final_context['unsmashable_files'], ['GSM1238108'])
def setUpClass(cls):
super(ESTestCases, cls).setUpClass() # ref https://stackoverflow.com/a/29655301/763705
"""Set up class."""
experiment = Experiment()
experiment.accession_code = "GSE000-X"
experiment.title = "NONONONO"
experiment.description = "Boooooourns. Wasabi."
experiment.technology = "RNA-SEQ"
experiment.save()
experiment = Experiment()
experiment.accession_code = "GSE123-X"
experiment.title = "Hey Ho Let's Go"
experiment.description = (
"This is a very exciting test experiment. Faygo soda. Blah blah blah."
)
experiment.technology = "MICROARRAY"
experiment.num_processed_samples = 1 # added below
experiment.num_total_samples = 1
experiment.num_downloadable_samples = 1
experiment.save()
experiment_annotation = ExperimentAnnotation()
experiment_annotation.data = {"hello": "world", "123": 456}
experiment_annotation.experiment = experiment
experiment_annotation.save()
sample = Sample()
sample.title = "123"
sample.accession_code = "123"
sample.save()
organism = Organism(
name="AILUROPODA_MELANOLEUCA", taxonomy_id=9646, is_scientific_name=True
)
organism.save()
sample = Sample()
sample.title = "789"
sample.accession_code = "789"
sample.is_processed = True
sample.organism = organism
sample.save()
sample_annotation = SampleAnnotation()
sample_annotation.data = {"goodbye": "world", "789": 123}
sample_annotation.sample = sample
sample_annotation.save()
original_file = OriginalFile()
original_file.save()
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.sample = sample
original_file_sample_association.original_file = original_file
original_file_sample_association.save()
downloader_job = DownloaderJob()
downloader_job.save()
download_assoc = DownloaderJobOriginalFileAssociation()
download_assoc.original_file = original_file
download_assoc.downloader_job = downloader_job
download_assoc.save()
processor_job = ProcessorJob()
processor_job.save()
processor_assoc = ProcessorJobOriginalFileAssociation()
processor_assoc.original_file = original_file
processor_assoc.processor_job = processor_job
processor_assoc.save()
# associate the experiment with the sample
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
result = ComputationalResult()
result.save()
# and create a qn tarjet for the sample
computational_result = ComputationalResultAnnotation()
computational_result.result = result
computational_result.data = {"is_qn": True, "organism_id": sample.organism.id}
computational_result.save()
# and associate it with the sample organism
sample.organism.qn_target = result
sample.organism.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
result = ComputationalResult()
result.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
# clear default cache and reindex
# otherwise the organisms with qn_targes will be cached.
cache.clear()
call_command("search_index", "--rebuild", "-f")
def test_get_results(self):
""" Test our ability to collect the appropriate samples. """
sample = Sample()
sample.accession_code = 'GSM45588'
sample.save()
result = ComputationalResult()
result.save()
computed_file1 = ComputedFile()
computed_file1.filename = "oh_boy.txt"
computed_file1.result = result
computed_file1.size_in_bytes = 123
computed_file1.is_smashable = True
computed_file1.save()
computed_file2 = ComputedFile()
computed_file2.filename = "gee_whiz.bmp"
computed_file2.result = result
computed_file2.size_in_bytes = 123
computed_file2.is_smashable = False
computed_file2.save()
assoc = SampleResultAssociation()
assoc.sample = sample
assoc.result = result
assoc.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file1
assoc.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file2
assoc.save()
computed_files = sample.get_result_files()
self.assertEqual(computed_files.count(), 2)
def test_dualtech_smash(self):
""" """
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1487313"
experiment.save()
result = ComputationalResult()
result.save()
gallus_gallus = Organism.get_object_for_name("GALLUS_GALLUS")
sample = Sample()
sample.accession_code = 'GSM1487313'
sample.title = 'GSM1487313'
sample.organism = gallus_gallus
sample.technology = "MICROARRAY"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1487313_liver.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
# RNASEQ TECH
experiment2 = Experiment()
experiment2.accession_code = "SRS332914"
experiment2.save()
result2 = ComputationalResult()
result2.save()
sample2 = Sample()
sample2.accession_code = 'SRS332914'
sample2.title = 'SRS332914'
sample2.organism = gallus_gallus
sample2.technology = "RNA-SEQ"
sample2.save()
sra2 = SampleResultAssociation()
sra2.sample = sample2
sra2.result = result2
sra2.save()
esa2 = ExperimentSampleAssociation()
esa2.experiment = experiment2
esa2.sample = sample2
esa2.save()
computed_file2 = ComputedFile()
computed_file2.filename = "SRP149598_gene_lengthScaledTPM.tsv"
computed_file2.absolute_file_path = "/home/user/data_store/PCL/" + computed_file2.filename
computed_file2.result = result2
computed_file2.size_in_bytes = 234
computed_file2.is_smashable = True
computed_file2.save()
assoc2 = SampleComputedFileAssociation()
assoc2.sample = sample2
assoc2.computed_file = computed_file2
assoc2.save()
# CROSS-SMASH BY SPECIES
ds = Dataset()
ds.data = {'GSE1487313': ['GSM1487313'], 'SRX332914': ['SRS332914']}
ds.aggregate_by = 'SPECIES'
ds.scale_by = 'STANDARD'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
self.assertTrue(ds.is_cross_technology())
final_context = smasher.smash(pj.pk, upload=False)
self.assertTrue(os.path.exists(final_context['output_file']))
os.remove(final_context['output_file'])
self.assertEqual(len(final_context['final_frame'].columns), 2)
# THEN BY EXPERIMENT
ds.aggregate_by = 'EXPERIMENT'
ds.save()
dsid = ds.id
ds = Dataset.objects.get(id=dsid)
pj.start_time = None
pj.end_time = None
pj.save()
final_context = smasher.smash(pj.pk, upload=False)
self.assertTrue(os.path.exists(final_context['output_file']))
os.remove(final_context['output_file'])
self.assertEqual(len(final_context['final_frame'].columns), 1)
# THEN BY ALL
ds.aggregate_by = 'ALL'
ds.save()
dsid = ds.id
ds = Dataset.objects.get(id=dsid)
pj.start_time = None
pj.end_time = None
pj.save()
final_context = smasher.smash(pj.pk, upload=False)
self.assertTrue(os.path.exists(final_context['output_file']))
self.assertEqual(len(final_context['final_frame'].columns), 2)
def setUp(self):
# Saving this for if we have protected endpoints
# self.superuser = User.objects.create_superuser('john', '*****@*****.**', 'johnpassword')
# self.client.login(username='******', password='******')
# self.user = User.objects.create(username="******")
experiment = Experiment()
experiment.accession_code = "GSE000"
experiment.alternate_accession_code = "E-GEOD-000"
experiment.title = "NONONONO"
experiment.description = "Boooooourns. Wasabi."
experiment.technology = "RNA-SEQ"
experiment.save()
experiment = Experiment()
experiment.accession_code = "GSE123"
experiment.title = "Hey Ho Let's Go"
experiment.description = (
"This is a very exciting test experiment. Faygo soda. Blah blah blah."
)
experiment.technology = "MICROARRAY"
experiment.save()
self.experiment = experiment
experiment_annotation = ExperimentAnnotation()
experiment_annotation.data = {"hello": "world", "123": 456}
experiment_annotation.experiment = experiment
experiment_annotation.save()
# Create 26 test organisms numbered 0-25 for pagination test, so there should be 29 organisms total (with the 3 others below)
for i in range(26):
Organism(name=("TEST_ORGANISM_{}".format(i)),
taxonomy_id=(1234 + i)).save()
ailuropoda = Organism(name="AILUROPODA_MELANOLEUCA",
taxonomy_id=9646,
is_scientific_name=True)
ailuropoda.save()
self.homo_sapiens = Organism(name="HOMO_SAPIENS",
taxonomy_id=9606,
is_scientific_name=True)
self.homo_sapiens.save()
self.danio_rerio = Organism(name="DANIO_RERIO",
taxonomy_id=1337,
is_scientific_name=True)
self.danio_rerio.save()
sample = Sample()
sample.title = "123"
sample.accession_code = "123"
sample.is_processed = True
sample.organism = ailuropoda
sample.save()
sample = Sample()
sample.title = "789"
sample.accession_code = "789"
sample.is_processed = True
sample.organism = ailuropoda
sample.save()
self.sample = sample
# add qn target for sample organism
result = ComputationalResult()
result.commands.append("create_qn_target.py")
result.is_ccdl = True
result.is_public = True
result.processor = None
result.save()
cra = ComputationalResultAnnotation()
cra.result = result
cra.data = {"organism_id": ailuropoda.id, "is_qn": True}
cra.save()
ailuropoda.qn_target = result
ailuropoda.save()
sample_annotation = SampleAnnotation()
sample_annotation.data = {"goodbye": "world", "789": 123}
sample_annotation.sample = sample
sample_annotation.save()
original_file = OriginalFile()
original_file.save()
original_file_sample_association = OriginalFileSampleAssociation()
original_file_sample_association.sample = sample
original_file_sample_association.original_file = original_file
original_file_sample_association.save()
downloader_job = DownloaderJob()
downloader_job.save()
download_assoc = DownloaderJobOriginalFileAssociation()
download_assoc.original_file = original_file
download_assoc.downloader_job = downloader_job
download_assoc.save()
processor_job = ProcessorJob()
processor_job.save()
processor_assoc = ProcessorJobOriginalFileAssociation()
processor_assoc.original_file = original_file
processor_assoc.processor_job = processor_job
processor_assoc.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample
experiment_sample_association.experiment = experiment
experiment_sample_association.save()
experiment.num_total_samples = 1
experiment.num_processed_samples = 1
experiment.save()
result = ComputationalResult()
result.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
result = ComputationalResult()
result.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
processor = Processor()
processor.name = "Salmon Quant"
processor.version = "v9.9.9"
processor.docker_image = "dr_salmon"
processor.environment = '{"some": "environment"}'
processor.save()
computational_result_short = ComputationalResult(processor=processor)
computational_result_short.save()
organism_index = OrganismIndex()
organism_index.index_type = "TRANSCRIPTOME_SHORT"
organism_index.organism = self.danio_rerio
organism_index.result = computational_result_short
organism_index.absolute_directory_path = (
"/home/user/data_store/salmon_tests/TRANSCRIPTOME_INDEX/SHORT")
organism_index.is_public = True
organism_index.s3_url = "not_blank"
organism_index.save()
return
def test_create_compendia(self):
job = ProcessorJob()
job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value
job.save()
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1487313"
experiment.save()
result = ComputationalResult()
result.save()
gallus_gallus = Organism.get_object_for_name("GALLUS_GALLUS",
taxonomy_id=1001)
sample = Sample()
sample.accession_code = "GSM1487313"
sample.title = "GSM1487313"
sample.organism = gallus_gallus
sample.technology = "MICROARRAY"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1487313_liver.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
# Missing sample that will be filtered
sample = Sample()
sample.accession_code = "GSM1487222"
sample.title = "this sample will be filtered"
sample.organism = gallus_gallus
sample.technology = "MICROARRAY"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1487222_empty.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/doesnt_exists.PCL"
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
# RNASEQ TECH
experiment2 = Experiment()
experiment2.accession_code = "SRS332914"
experiment2.save()
result2 = ComputationalResult()
result2.save()
sample2 = Sample()
sample2.accession_code = "SRS332914"
sample2.title = "SRS332914"
sample2.organism = gallus_gallus
sample2.technology = "RNA-SEQ"
sample2.save()
sra2 = SampleResultAssociation()
sra2.sample = sample2
sra2.result = result2
sra2.save()
esa2 = ExperimentSampleAssociation()
esa2.experiment = experiment2
esa2.sample = sample2
esa2.save()
computed_file2 = ComputedFile()
computed_file2.filename = "SRP149598_gene_lengthScaledTPM.tsv"
computed_file2.absolute_file_path = "/home/user/data_store/PCL/" + computed_file2.filename
computed_file2.result = result2
computed_file2.size_in_bytes = 234
computed_file2.is_smashable = True
computed_file2.save()
assoc2 = SampleComputedFileAssociation()
assoc2.sample = sample2
assoc2.computed_file = computed_file2
assoc2.save()
dset = Dataset()
dset.data = {
"GSE1487313": ["GSM1487313", "GSM1487222"],
"SRX332914": ["SRS332914"]
}
dset.scale_by = "NONE"
dset.aggregate_by = "SPECIES"
dset.svd_algorithm = "ARPACK"
dset.quantile_normalize = False
dset.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = dset
pjda.save()
final_context = create_compendia.create_compendia(job.id)
self.assertFalse(job.success)
# check that sample with no computed file was skipped
self.assertTrue("GSM1487222" in final_context["filtered_samples"])
self.assertEqual(
final_context["filtered_samples"]["GSM1487222"]
["experiment_accession_code"],
"GSE1487313",
)
def test_get_tximport_inputs(self):
""""Tests that tximport only considers RNA-Seq samples from GEO.
"""
# Create one experiment and two related samples, based on:
# https://www.ncbi.nlm.nih.gov/sra/?term=SRP040623
# (We don't need any original files because
# get_tximport_inputs doesn't consider them.)
experiment_accession = 'PRJNA242809'
experiment = Experiment.objects.create(accession_code=experiment_accession)
c_elegans = Organism.get_object_for_name("CAENORHABDITIS_ELEGANS")
## Sample 1
sample1_accession = 'SRR1206053'
sample1 = Sample.objects.create(accession_code=sample1_accession,
organism=c_elegans)
sample1.source_database = 'GEO'
sample1.technology = 'RNA-SEQ'
ExperimentSampleAssociation.objects.create(experiment=experiment, sample=sample1)
## Sample 2
sample2_accession = 'SRR1206054'
sample2 = Sample.objects.create(accession_code=sample2_accession,
organism=c_elegans)
sample2.source_database = 'GEO'
sample2.technology = 'RNA-SEQ'
ExperimentSampleAssociation.objects.create(experiment=experiment, sample=sample2)
computational_result1 = ComputationalResult(processor=utils.find_processor('SALMON_QUANT'))
computational_result1.save()
sample_result_assoc = SampleResultAssociation(sample=sample1, result=computational_result1)
sample_result_assoc.save()
comp_file = ComputedFile()
comp_file.absolute_file_path = "/doesnt/matter"
comp_file.result = computational_result1
comp_file.size_in_bytes=1337
comp_file.sha1="ABC"
comp_file.s3_key = "key"
comp_file.s3_bucket = "bucket"
comp_file.save()
computational_result2 = ComputationalResult(processor=utils.find_processor('SALMON_QUANT'))
computational_result2.save()
sample_result_assoc = SampleResultAssociation(sample=sample2, result=computational_result2)
sample_result_assoc.save()
comp_file = ComputedFile()
comp_file.absolute_file_path = "/doesnt/matter"
comp_file.result = computational_result2
comp_file.size_in_bytes=1337
comp_file.sha1="ABC"
comp_file.s3_key = "key"
comp_file.s3_bucket = "bucket"
comp_file.save()
quantified_experiments = salmon.get_tximport_inputs({"sample": sample1})['tximport_inputs']
self.assertEqual({}, quantified_experiments)
def test_create_compendia(self):
job = ProcessorJob()
job.pipeline_applied = "COMPENDIA"
job.save()
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1487313"
experiment.save()
result = ComputationalResult()
result.save()
gallus_gallus = Organism.get_object_for_name("GALLUS_GALLUS")
sample = Sample()
sample.accession_code = 'GSM1487313'
sample.title = 'GSM1487313'
sample.organism = gallus_gallus
sample.technology = "MICROARRAY"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1487313_liver.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
# RNASEQ TECH
experiment2 = Experiment()
experiment2.accession_code = "SRS332914"
experiment2.save()
result2 = ComputationalResult()
result2.save()
sample2 = Sample()
sample2.accession_code = 'SRS332914'
sample2.title = 'SRS332914'
sample2.organism = gallus_gallus
sample2.technology = "RNA-SEQ"
sample2.save()
sra2 = SampleResultAssociation()
sra2.sample = sample2
sra2.result = result2
sra2.save()
esa2 = ExperimentSampleAssociation()
esa2.experiment = experiment2
esa2.sample = sample2
esa2.save()
computed_file2 = ComputedFile()
computed_file2.filename = "SRP149598_gene_lengthScaledTPM.tsv"
computed_file2.absolute_file_path = "/home/user/data_store/PCL/" + computed_file2.filename
computed_file2.result = result2
computed_file2.size_in_bytes = 234
computed_file2.is_smashable = True
computed_file2.save()
assoc2 = SampleComputedFileAssociation()
assoc2.sample = sample2
assoc2.computed_file = computed_file2
assoc2.save()
dset = Dataset()
dset.data = {'GSE1487313': ['GSM1487313'], 'SRX332914': ['SRS332914']}
dset.scale_by = 'NONE'
dset.aggregate_by = 'SPECIES'
dset.quantile_normalize = False
dset.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = dset
pjda.save()
final_context = create_compendia.create_compendia(job.id)
def test_create_compendia_danio(self):
job = ProcessorJob()
job.pipeline_applied = ProcessorPipeline.CREATE_COMPENDIA.value
job.save()
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1234"
experiment.save()
result = ComputationalResult()
result.save()
qn_target = ComputedFile()
qn_target.filename = "danio_target.tsv"
qn_target.absolute_file_path = "/home/user/data_store/QN/danio_target.tsv"
qn_target.is_qn_target = True
qn_target.size_in_bytes = "12345"
qn_target.sha1 = "aabbccddeeff"
qn_target.result = result
qn_target.save()
danio_rerio = Organism(name="DANIO_RERIO",
taxonomy_id=1,
qn_target=result)
danio_rerio.save()
cra = ComputationalResultAnnotation()
cra.data = {}
cra.data["organism_id"] = danio_rerio.id
cra.data["is_qn"] = True
cra.result = result
cra.save()
result = ComputationalResult()
result.save()
micros = []
for file in os.listdir("/home/user/data_store/raw/TEST/MICROARRAY/"):
if "microarray.txt" in file:
continue
sample = Sample()
sample.accession_code = file
sample.title = file
sample.organism = danio_rerio
sample.technology = "MICROARRAY"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = file
computed_file.absolute_file_path = "/home/user/data_store/raw/TEST/MICROARRAY/" + file
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
micros.append(file)
experiment = Experiment()
experiment.accession_code = "GSE5678"
experiment.save()
result = ComputationalResult()
result.save()
rnas = []
for file in os.listdir("/home/user/data_store/raw/TEST/RNASEQ/"):
if "rnaseq.txt" in file:
continue
sample = Sample()
sample.accession_code = file
sample.title = file
sample.organism = danio_rerio
sample.technology = "RNASEQ"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = file
computed_file.absolute_file_path = "/home/user/data_store/raw/TEST/RNASEQ/" + file
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
rnas.append(file)
# Missing sample that will be filtered
sample = Sample()
sample.accession_code = "GSM1487222"
sample.title = "this sample will be filtered"
sample.organism = danio_rerio
sample.technology = "RNASEQ"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
rnas.append(sample.accession_code)
dset = Dataset()
dset.data = {"GSE1234": micros, "GSE5678": rnas}
dset.scale_by = "NONE"
dset.aggregate_by = "SPECIES"
dset.svd_algorithm = "ARPACK"
dset.quantile_normalize = False
dset.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = dset
pjda.save()
final_context = create_compendia.create_compendia(job.id)
# Verify result
self.assertEqual(
final_context["compendium_result"].result.computedfile_set.count(),
1)
for file in final_context[
"compendium_result"].result.computedfile_set.all():
self.assertTrue(os.path.exists(file.absolute_file_path))
# test compendium_result
self.assertEqual(final_context["compendium_result"].svd_algorithm,
"ARPACK")
self.assertEqual(
final_context["compendium_result"].primary_organism.name,
final_context["organism_name"])
self.assertEqual(
final_context["compendium_result"].primary_organism.name,
"DANIO_RERIO")
self.assertEqual(final_context["compendium_result"].organisms.count(),
1)
# check that sample with no computed file was skipped
self.assertTrue("GSM1487222" in final_context["filtered_samples"])
self.assertEqual(
final_context["filtered_samples"]["GSM1487222"]
["experiment_accession_code"], "GSE5678")
def test_create_compendia_danio(self):
job = ProcessorJob()
job.pipeline_applied = "COMPENDIA"
job.save()
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1234"
experiment.save()
result = ComputationalResult()
result.save()
danio_rerio = Organism.get_object_for_name("DANIO_RERIO")
micros = []
for file in os.listdir('/home/user/data_store/raw/TEST/MICROARRAY/'):
if 'microarray.txt' in file:
continue
sample = Sample()
sample.accession_code = file
sample.title = file
sample.organism = danio_rerio
sample.technology = "MICROARRAY"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = file
computed_file.absolute_file_path = "/home/user/data_store/raw/TEST/MICROARRAY/" + file
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
micros.append(file)
experiment = Experiment()
experiment.accession_code = "GSE5678"
experiment.save()
result = ComputationalResult()
result.save()
rnas = []
for file in os.listdir('/home/user/data_store/raw/TEST/RNASEQ/'):
if 'rnaseq.txt' in file:
continue
sample = Sample()
sample.accession_code = file
sample.title = file
sample.organism = danio_rerio
sample.technology = "RNASEQ"
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = file
computed_file.absolute_file_path = "/home/user/data_store/raw/TEST/RNASEQ/" + file
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
rnas.append(file)
result = ComputationalResult()
result.save()
qn_target = ComputedFile()
qn_target.filename = "danio_target.tsv"
qn_target.absolute_file_path = '/home/user/data_store/QN/danio_target.tsv'
qn_target.is_qn_target = True
qn_target.size_in_bytes = "12345"
qn_target.sha1 = "aabbccddeeff"
qn_target.result = result
qn_target.save()
cra = ComputationalResultAnnotation()
cra.data = {}
cra.data['organism_id'] = danio_rerio.id
cra.data['is_qn'] = True
cra.result = result
cra.save()
dset = Dataset()
dset.data = {'GSE1234': micros, 'GSE5678': rnas}
dset.scale_by = 'NONE'
dset.aggregate_by = 'SPECIES'
dset.quantile_normalize = False
dset.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = dset
pjda.save()
final_context = create_compendia.create_compendia(job.id)
# Verify result
self.assertEqual(len(final_context['computed_files']), 3)
for file in final_context['computed_files']:
self.assertTrue(os.path.exists(file.absolute_file_path))
def _run_salmontools(job_context: Dict) -> Dict:
""" Run Salmontools to extract unmapped genes. """
logger.debug("Running SalmonTools ...")
unmapped_filename = job_context[
"output_directory"] + "aux_info/unmapped_names.txt"
command_str = "salmontools extract-unmapped -u {unmapped_file} -o {output} "
output_prefix = job_context["salmontools_directory"] + "unmapped_by_salmon"
command_str = command_str.format(unmapped_file=unmapped_filename,
output=output_prefix)
if "input_file_path_2" in job_context:
command_str += "-1 {input_1} -2 {input_2}"
command_str = command_str.format(
input_1=job_context["input_file_path"],
input_2=job_context["input_file_path_2"])
else:
command_str += "-r {input_1}"
command_str = command_str.format(
input_1=job_context["input_file_path"])
start_time = timezone.now()
logger.debug(
"Running the following SalmonTools command: %s",
command_str,
processor_job=job_context["job_id"],
)
completed_command = subprocess.run(command_str.split(),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
end_time = timezone.now()
# As of SalmonTools 0.1.0, completed_command.returncode is always 0,
# (even if error happens). completed_command.stderr is not totally
# reliable either, because it will output the following line even
# when the execution succeeds:
# "There were <N> unmapped reads\n"
# in which "<N>" is the number of lines in input unmapped_names.txt.
#
# As a workaround, we are using a regular expression here to test
# the status of SalmonTools execution. Any text in stderr that is
# not in the above format is treated as error message.
status_str = completed_command.stderr.decode().strip()
success_pattern = r"^There were \d+ unmapped reads$"
if re.match(success_pattern, status_str):
# Zip up the output of salmontools
try:
with tarfile.open(job_context["salmontools_archive"],
"w:gz") as tar:
tar.add(job_context["salmontools_directory"], arcname=os.sep)
except Exception:
logger.exception(
"Exception caught while zipping processed directory %s",
job_context["salmontools_directory"],
processor_job=job_context["job_id"],
)
failure_template = "Exception caught while zipping salmontools directory {}"
job_context["job"].failure_reason = failure_template.format(
job_context["salmontools_archive"])
job_context["success"] = False
return job_context
result = ComputationalResult()
result.commands.append(command_str)
result.time_start = start_time
result.time_end = end_time
result.is_ccdl = True
try:
processor_key = "SALMONTOOLS"
result.processor = utils.find_processor(processor_key)
except Exception as e:
return utils.handle_processor_exception(job_context, processor_key,
e)
result.save()
job_context["pipeline"].steps.append(result.id)
assoc = SampleResultAssociation()
assoc.sample = job_context["sample"]
assoc.result = result
assoc.save()
computed_file = ComputedFile()
computed_file.filename = job_context["salmontools_archive"].split(
"/")[-1]
computed_file.absolute_file_path = job_context["salmontools_archive"]
computed_file.calculate_sha1()
computed_file.calculate_size()
computed_file.is_public = True
computed_file.is_smashable = False
computed_file.is_qc = True
computed_file.result = result
computed_file.save()
job_context["computed_files"].append(computed_file)
assoc = SampleComputedFileAssociation()
assoc.sample = job_context["sample"]
assoc.computed_file = computed_file
assoc.save()
job_context["result"] = result
job_context["success"] = True
else: # error in salmontools
logger.error(
"Shell call to salmontools failed with error message: %s",
status_str,
processor_job=job_context["job_id"],
)
job_context["job"].failure_reason = (
"Shell call to salmontools failed because: " + status_str)
job_context["success"] = False
return job_context
def prepare_computed_files():
# MICROARRAY TECH
experiment = Experiment()
experiment.accession_code = "GSE1487313"
experiment.num_processed_samples = 1
experiment.save()
result = ComputationalResult()
result.save()
gallus_gallus = Organism.get_object_for_name("GALLUS_GALLUS",
taxonomy_id=1001)
sample = Sample()
sample.accession_code = "GSM1487313"
sample.title = "GSM1487313"
sample.organism = gallus_gallus
sample.technology = "MICROARRAY"
sample.is_processed = True
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1487313_liver.PCL"
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.s3_key = "GSM1487313_liver.PCL"
computed_file.s3_bucket = TEST_DATA_BUCKET
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
# RNASEQ TECH
experiment2 = Experiment()
experiment2.accession_code = "SRP332914"
experiment2.num_processed_samples = 1
experiment2.save()
result2 = ComputationalResult()
result2.save()
sample2 = Sample()
sample2.accession_code = "SRR332914"
sample2.title = "SRR332914"
sample2.organism = gallus_gallus
sample2.technology = "RNA-SEQ"
sample2.is_processed = True
sample2.save()
sra2 = SampleResultAssociation()
sra2.sample = sample2
sra2.result = result2
sra2.save()
esa2 = ExperimentSampleAssociation()
esa2.experiment = experiment2
esa2.sample = sample2
esa2.save()
computed_file2 = ComputedFile()
computed_file2.filename = "SRP149598_gene_lengthScaledTPM.tsv"
computed_file2.result = result2
computed_file2.size_in_bytes = 234
computed_file2.is_smashable = True
computed_file2.s3_key = "SRP149598_gene_lengthScaledTPM.tsv"
computed_file2.s3_bucket = TEST_DATA_BUCKET
computed_file2.save()
assoc2 = SampleComputedFileAssociation()
assoc2.sample = sample2
assoc2.computed_file = computed_file2
assoc2.save()
def test_no_smash_dupe_two(self):
""" Tests the SRP051449 case, where the titles collide. Also uses a real QN target file."""
job = ProcessorJob()
job.pipeline_applied = "SMASHER"
job.save()
experiment = Experiment()
experiment.accession_code = "SRP051449"
experiment.save()
result = ComputationalResult()
result.save()
danio_rerio = Organism.get_object_for_name("DANIO_RERIO")
sample = Sample()
sample.accession_code = 'SRR1731761'
sample.title = 'Danio rerio'
sample.organism = danio_rerio
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "SRR1731761_output_gene_lengthScaledTPM.tsv"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
result = ComputationalResult()
result.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sample = Sample()
sample.accession_code = 'SRR1731762'
sample.title = 'Danio rerio'
sample.organism = danio_rerio
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "SRR1731762_output_gene_lengthScaledTPM.tsv"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
result = ComputationalResult()
result.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'SRP051449': ['SRR1731761', 'SRR1731762']}
ds.aggregate_by = 'SPECIES'
ds.scale_by = 'NONE'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = True
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = job
pjda.dataset = ds
pjda.save()
cr = ComputationalResult()
cr.save()
computed_file = ComputedFile()
computed_file.filename = "danio_target.tsv"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = cr
computed_file.size_in_bytes = 123
computed_file.is_smashable = False
computed_file.save()
cra = ComputationalResultAnnotation()
cra.data = {'organism_id': danio_rerio.id, 'is_qn': True}
cra.result = cr
cra.save()
final_context = smasher.smash(job.pk, upload=False)
self.assertTrue(final_context['success'])
def _tximport(job_context: Dict, experiment: Experiment, quant_files: List[ComputedFile]) -> Dict:
"""Run tximport R script based on input quant files and the path
of genes_to_transcripts.txt.
"""
# Download all the quant.sf fles for this experiment. Write all
# their paths to a file so we can pass a path to that to
# tximport.R rather than having to pass in one argument per
# sample.
tximport_path_list_file = job_context["work_dir"] + "tximport_inputs.txt"
with open(tximport_path_list_file, "w") as input_list:
for quant_file in quant_files:
input_list.write(quant_file.get_synced_file_path() + "\n")
rds_filename = "txi_out.RDS"
rds_file_path = job_context["work_dir"] + rds_filename
tpm_filename = "gene_lengthScaledTPM.tsv"
tpm_file_path = job_context["work_dir"] + tpm_filename
result = ComputationalResult()
cmd_tokens = [
"/usr/bin/Rscript", "--vanilla",
"/home/user/data_refinery_workers/processors/tximport.R",
"--file_list", tximport_path_list_file,
"--gene2txmap", job_context["genes_to_transcripts_path"],
"--rds_file", rds_file_path,
"--tpm_file", tpm_file_path
]
result.time_start = timezone.now()
logger.debug("Running tximport with: %s",
str(cmd_tokens),
processor_job=job_context['job_id'],
experiment=experiment.id)
try:
tximport_result = subprocess.run(cmd_tokens, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
except Exception as e:
error_template = ("Encountered error in R code while running tximport.R: {}")
error_message = error_template.format(str(e))
logger.error(error_message, processor_job=job_context["job_id"], experiment=experiment.id)
job_context["job"].failure_reason = error_message
job_context["success"] = False
return job_context
if tximport_result.returncode != 0:
error_template = ("Found non-zero exit code from R code while running tximport.R: {}")
error_message = error_template.format(tximport_result.stderr.decode().strip())
logger.error(error_message, processor_job=job_context["job_id"], experiment=experiment.id)
job_context["job"].failure_reason = error_message
job_context["success"] = False
return job_context
result.time_end = timezone.now()
result.commands.append(" ".join(cmd_tokens))
result.is_ccdl = True
try:
processor_key = "TXIMPORT"
result.processor = utils.find_processor(processor_key)
except Exception as e:
return utils.handle_processor_exception(job_context, processor_key, e)
result.save()
job_context['pipeline'].steps.append(result.id)
# Associate this result with all samples in this experiment.
# TODO: This may not be completely sensible, because `tximport` is
# done at experiment level, not at sample level.
# Could be very problematic if SRA's data model allows many
# Experiments to one Run.
# https://github.com/AlexsLemonade/refinebio/issues/297
for sample in experiment.samples.all():
s_r = SampleResultAssociation(sample=sample, result=result)
s_r.save()
rds_file = ComputedFile()
rds_file.absolute_file_path = rds_file_path
rds_file.filename = rds_filename
rds_file.result = result
rds_file.is_smashable = False
rds_file.is_qc = False
rds_file.is_public = True
rds_file.calculate_sha1()
rds_file.calculate_size()
rds_file.save()
job_context['computed_files'].append(rds_file)
# Split the tximport result into smashable subfiles
data = pd.read_csv(tpm_file_path, sep='\t', header=0, index_col=0)
individual_files = []
frames = np.split(data, len(data.columns), axis=1)
for frame in frames:
# Create sample-specific TPM file.
sample_file_name = frame.columns.values[0] + '_' + tpm_filename
frame_path = os.path.join(job_context["work_dir"], sample_file_name)
frame.to_csv(frame_path, sep='\t', encoding='utf-8')
# The frame column header is based off of the path, which includes _output.
sample = Sample.objects.get(accession_code=frame.columns.values[0].replace("_output", ""))
computed_file = ComputedFile()
computed_file.absolute_file_path = frame_path
computed_file.filename = sample_file_name
computed_file.result = result
computed_file.is_smashable = True
computed_file.is_qc = False
computed_file.is_public = True
computed_file.calculate_sha1()
computed_file.calculate_size()
computed_file.save()
job_context['computed_files'].append(computed_file)
job_context['smashable_files'].append(computed_file)
SampleResultAssociation.objects.get_or_create(
sample=sample,
result=result)
# Create association with the RDS file.
SampleComputedFileAssociation.objects.get_or_create(
sample=sample,
computed_file=rds_file)
# Create association with TPM file.
SampleComputedFileAssociation.objects.get_or_create(
sample=sample,
computed_file=computed_file)
individual_files.append(computed_file)
job_context['samples'].append(sample)
# Clean up quant.sf files that were created just for this.
for quant_file in quant_files:
quant_file.delete_s3_file()
# It's only okay to delete the local file because the full
# output directory has already been zipped up.
quant_file.delete_local_file()
quant_file.delete()
# Salmon-processed samples aren't marked as is_processed
# until they are fully tximported, this value sets that
# for the end_job function.
job_context['tximported'] = True
job_context['individual_files'] = individual_files
return job_context
def test_log2(self):
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
# Has non-log2 data:
# https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44421
# ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE44nnn/GSE44421/miniml/GSE44421_family.xml.tgz
experiment = Experiment()
experiment.accession_code = "GSE44421"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
sample = Sample()
sample.accession_code = 'GSM1084806'
sample.title = 'GSM1084806'
sample.organism = homo_sapiens
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1084806-tbl-1.txt"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sample = Sample()
sample.accession_code = 'GSM1084807'
sample.title = 'GSM1084807'
sample.organism = homo_sapiens
sample.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1084807-tbl-1.txt"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE44421': ['GSM1084806', 'GSM1084807']}
ds.aggregate_by = 'EXPERIMENT'
ds.scale_by = 'MINMAX'
ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(pj.pk, upload=False)
ds = Dataset.objects.get(id=ds.id)
self.assertTrue(final_context['success'])
def test_bad_overlap(self):
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
experiment = Experiment()
experiment.accession_code = "GSE51081"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
sample = Sample()
sample.accession_code = 'GSM1237810'
sample.title = 'GSM1237810'
sample.organism = homo_sapiens
sample.save()
sample_annotation = SampleAnnotation()
sample_annotation.data = {'hi': 'friend'}
sample_annotation.sample = sample
sample_annotation.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "big.PCL"
computed_file.absolute_file_path = "/home/user/data_store/BADSMASH/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sample = Sample()
sample.accession_code = 'GSM1237812'
sample.title = 'GSM1237812'
sample.organism = homo_sapiens
sample.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
computed_file = ComputedFile()
computed_file.filename = "small.PCL"
computed_file.absolute_file_path = "/home/user/data_store/BADSMASH/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['GSM1237810', 'GSM1237812']}
ds.aggregate_by = 'ALL' # [ALL or SPECIES or EXPERIMENT]
ds.scale_by = 'NONE' # [NONE or MINMAX or STANDARD or ROBUST]
ds.email_address = "*****@*****.**"
#ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(pj.pk, upload=False)
ds = Dataset.objects.get(id=ds.id)
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
# Now, make sure the bad can't zero this out.
sample = Sample()
sample.accession_code = 'GSM999'
sample.title = 'GSM999'
sample.organism = homo_sapiens
sample.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
computed_file = ComputedFile()
computed_file.filename = "bad.PCL"
computed_file.absolute_file_path = "/home/user/data_store/BADSMASH/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['GSM1237810', 'GSM1237812', 'GSM999']}
ds.aggregate_by = 'ALL' # [ALL or SPECIES or EXPERIMENT]
ds.scale_by = 'NONE' # [NONE or MINMAX or STANDARD or ROBUST]
ds.email_address = "*****@*****.**"
#ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
final_context = smasher.smash(pj.pk, upload=False)
ds = Dataset.objects.get(id=ds.id)
self.assertEqual(len(final_context['final_frame']), 4)
def test_dataset_adding_non_downloadable_samples_fails(self):
# Make a sample that is not downloadable
sample1 = Sample()
sample1.title = "456"
sample1.accession_code = "456"
sample1.platform_name = "AFFY"
sample1.is_processed = False
sample1.organism = self.homo_sapiens
sample1.save()
experiment_sample_association = ExperimentSampleAssociation()
experiment_sample_association.sample = sample1
experiment_sample_association.experiment = self.experiment
experiment_sample_association.save()
# Bad, 456 is not processed
jdata = json.dumps({
"email_address": "*****@*****.**",
"data": {
"GSE123": ["456"]
}
})
response = self.client.post(
reverse("create_dataset", kwargs={"version": API_VERSION}),
jdata,
content_type="application/json",
)
self.assertEqual(response.status_code, 400)
self.assertIn(
"Non-downloadable sample(s) in dataset",
response.json()["message"],
)
self.assertEqual(response.json()["details"], ["456"])
cache.clear()
# Bad, 567 does not exist
jdata = json.dumps({
"email_address": "*****@*****.**",
"data": {
"GSE123": ["567"]
}
})
response = self.client.post(
reverse("create_dataset", kwargs={"version": API_VERSION}),
jdata,
content_type="application/json",
)
self.assertIn(
"Sample(s) in dataset do not exist on refine",
response.json()["message"],
)
self.assertEqual(response.status_code, 400)
cache.clear()
# Good, 789 is processed
jdata = json.dumps({
"email_address": "*****@*****.**",
"data": {
"GSE123": ["789"]
}
})
response = self.client.post(
reverse("create_dataset", kwargs={"version": API_VERSION}),
jdata,
content_type="application/json",
)
self.assertEqual(response.status_code, 201)
cache.clear()
# Bad, 456 does not have a quant.sf file
post_data = {"email_address": "*****@*****.**", "data": {}}
response = self.client.post(
reverse("create_dataset", kwargs={"version": API_VERSION}),
json.dumps(post_data),
content_type="application/json",
)
self.assertEqual(response.status_code, 201)
cache.clear()
put_data = {
**post_data, "data": {
"GSE123": ["456"]
},
"quant_sf_only": True
}
response = self.client.put(
reverse("dataset",
kwargs={
"id": response.json()["id"],
"version": API_VERSION
}),
json.dumps(put_data),
content_type="application/json",
)
self.assertEqual(response.status_code, 400)
self.assertIn(
"Sample(s) in dataset are missing quant.sf files",
response.json()["message"],
)
self.assertEqual(response.json()["details"], ["456"])
cache.clear()
# Bad, none of the samples in GSE123 have a quant.sf file
response = self.client.post(
reverse("create_dataset", kwargs={"version": API_VERSION}),
json.dumps(post_data),
content_type="application/json",
)
self.assertEqual(response.status_code, 201)
cache.clear()
response = self.client.put(
reverse("dataset",
kwargs={
"id": response.json()["id"],
"version": API_VERSION
}),
json.dumps({
**put_data, "data": {
"GSE123": ["ALL"]
}
}),
content_type="application/json",
)
self.assertEqual(response.status_code, 400)
self.assertIn(
"Experiment(s) in dataset have zero downloadable samples",
response.json()["message"],
)
self.assertEqual(response.json()["details"], ["GSE123"])
cache.clear()
# Make 456 have a quant.sf file
result = ComputationalResult()
result.save()
sra = SampleResultAssociation()
sra.sample = sample1
sra.result = result
sra.save()
computed_file = ComputedFile()
computed_file.s3_key = "smasher-test-quant.sf"
computed_file.s3_bucket = "data-refinery-test-assets"
computed_file.filename = "quant.sf"
computed_file.result = result
computed_file.size_in_bytes = 42
computed_file.save()
# Good, 456 does have a quant.sf file
response = self.client.post(
reverse("create_dataset", kwargs={"version": API_VERSION}),
json.dumps(post_data),
content_type="application/json",
)
self.assertEqual(response.status_code, 201)
cache.clear()
response = self.client.put(
reverse("dataset",
kwargs={
"id": response.json()["id"],
"version": API_VERSION
}),
json.dumps(put_data),
content_type="application/json",
)
self.assertEqual(response.status_code, 200)
cache.clear()
# Good, a sample in GSE123 has a quant.sf file
response = self.client.post(
reverse("create_dataset", kwargs={"version": API_VERSION}),
json.dumps(post_data),
content_type="application/json",
)
cache.clear()
self.assertEqual(response.status_code, 201)
response = self.client.put(
reverse("dataset",
kwargs={
"id": response.json()["id"],
"version": API_VERSION
}),
json.dumps({
**put_data, "data": {
"GSE123": ["ALL"]
}
}),
content_type="application/json",
)
self.assertEqual(response.status_code, 200)
def prepare_job():
pj = ProcessorJob()
pj.pipeline_applied = "SMASHER"
pj.save()
experiment = Experiment()
experiment.accession_code = "GSE51081"
experiment.save()
result = ComputationalResult()
result.save()
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS")
sample = Sample()
sample.accession_code = 'GSM1237810'
sample.title = 'GSM1237810'
sample.organism = homo_sapiens
sample.save()
sample_annotation = SampleAnnotation()
sample_annotation.data = {'hi': 'friend'}
sample_annotation.sample = sample
sample_annotation.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1237810_T09-1084.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sample = Sample()
sample.accession_code = 'GSM1237812'
sample.title = 'GSM1237812'
sample.organism = homo_sapiens
sample.save()
esa = ExperimentSampleAssociation()
esa.experiment = experiment
esa.sample = sample
esa.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
sra = SampleResultAssociation()
sra.sample = sample
sra.result = result
sra.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1237812_S97-PURE.PCL"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = True
computed_file.save()
computed_file = ComputedFile()
computed_file.filename = "GSM1237812_S97-PURE.DAT"
computed_file.absolute_file_path = "/home/user/data_store/PCL/" + computed_file.filename
computed_file.result = result
computed_file.size_in_bytes = 123
computed_file.is_smashable = False
computed_file.save()
assoc = SampleComputedFileAssociation()
assoc.sample = sample
assoc.computed_file = computed_file
assoc.save()
ds = Dataset()
ds.data = {'GSE51081': ['GSM1237810', 'GSM1237812']}
ds.aggregate_by = 'EXPERIMENT' # [ALL or SPECIES or EXPERIMENT]
ds.scale_by = 'STANDARD' # [NONE or MINMAX or STANDARD or ROBUST]
ds.email_address = "*****@*****.**"
#ds.email_address = "*****@*****.**"
ds.quantile_normalize = False
ds.save()
pjda = ProcessorJobDatasetAssociation()
pjda.processor_job = pj
pjda.dataset = ds
pjda.save()
return pj
def test_make_experiment_result_associations(self):
"""Tests that the correct associations are made.
The situation we're setting up is basically this:
* tximport has been run for an experiment.
* It made associations between the samples in
the experiment and the ComputationalResult.
* It didn't make associations between the
experiment itself and the ComputationalResult.
* There is a second experiment that hasn't had
tximport run but shares a sample with the
other experiment.
* This second experiment has a sample which has
not yet had tximport run on it.
And what we're going to test for is:
* An association is created between the tximport
result and the first experiment.
* An association is NOT created between the
tximport result and the second experiment.
"""
# Get an organism to set on samples:
homo_sapiens = Organism.get_object_for_name("HOMO_SAPIENS",
taxonomy_id=9606)
# Create the tximport processor and result:
processor = Processor()
processor.name = "Tximport"
processor.version = "v9.9.9"
processor.docker_image = "dr_salmon"
processor.environment = '{"some": "environment"}'
processor.save()
result = ComputationalResult()
result.commands.append("tximport invocation")
result.is_ccdl = True
result.processor = processor
result.save()
# Create the first experiment and it's samples:
processed_experiment = Experiment()
processed_experiment.accession_code = "SRP12345"
processed_experiment.save()
processed_sample_one = Sample()
processed_sample_one.accession_code = "SRX12345"
processed_sample_one.title = "SRX12345"
processed_sample_one.organism = homo_sapiens
processed_sample_one.save()
sra = SampleResultAssociation()
sra.sample = processed_sample_one
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = processed_experiment
esa.sample = processed_sample_one
esa.save()
processed_sample_two = Sample()
processed_sample_two.accession_code = "SRX12346"
processed_sample_two.title = "SRX12346"
processed_sample_two.organism = homo_sapiens
processed_sample_two.save()
sra = SampleResultAssociation()
sra.sample = processed_sample_two
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = processed_experiment
esa.sample = processed_sample_two
esa.save()
# Create the second experiment and it's additional sample.
unprocessed_experiment = Experiment()
unprocessed_experiment.accession_code = "SRP6789"
unprocessed_experiment.save()
unprocessed_sample = Sample()
unprocessed_sample.accession_code = "SRX6789"
unprocessed_sample.title = "SRX6789"
unprocessed_sample.organism = homo_sapiens
unprocessed_sample.save()
sra = SampleResultAssociation()
sra.sample = unprocessed_sample
sra.result = result
sra.save()
esa = ExperimentSampleAssociation()
esa.experiment = unprocessed_experiment
esa.sample = unprocessed_sample
esa.save()
esa = ExperimentSampleAssociation()
esa.experiment = unprocessed_experiment
esa.sample = processed_sample_two
esa.save()
# Run the function we're testing:
make_experiment_result_associations()
# Test that only one association was created and that it was
# to the processed experiment:
eras = ExperimentResultAssociation.objects.all()
self.assertEqual(len(eras), 1)
self.assertEqual(eras.first().experiment, processed_experiment)
def _run_multiqc(job_context: Dict) -> Dict:
"""Runs the `MultiQC` package to generate the QC report.
TODO: These seem to consume a lot of RAM, even for small files.
We should consider tuning these or breaking them out into their
own processors. JVM settings may reduce RAM footprint.
"""
command_str = ("multiqc {input_directory} --outdir {qc_directory} --zip-data-dir")
formatted_command = command_str.format(input_directory=job_context["qc_input_directory"],
qc_directory=job_context["qc_directory"])
logger.debug("Running MultiQC using the following shell command: %s",
formatted_command,
processor_job=job_context["job_id"])
qc_env = os.environ.copy()
qc_env["LC_ALL"] = "C.UTF-8"
qc_env["LANG"] = "C.UTF-8"
time_start = timezone.now()
completed_command = subprocess.run(formatted_command.split(),
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
env=qc_env)
time_end = timezone.now()
if completed_command.returncode != 0:
stderr = completed_command.stderr.decode().strip()
error_start = stderr.upper().find("ERROR:")
error_start = error_start if error_start != -1 else 0
logger.error("Shell call to MultiQC failed with error message: %s",
stderr[error_start:],
processor_job=job_context["job_id"])
job_context["job"].failure_reason = ("Shell call to MultiQC failed because: "
+ stderr[error_start:])
job_context["success"] = False
result = ComputationalResult()
result.commands.append(formatted_command)
result.time_start = time_start
result.time_end = time_end
result.is_ccdl = True
try:
processor_key = "MULTIQC"
result.processor = utils.find_processor(processor_key)
except Exception as e:
return utils.handle_processor_exception(job_context, processor_key, e)
result.save()
job_context['pipeline'].steps.append(result.id)
assoc = SampleResultAssociation()
assoc.sample = job_context["sample"]
assoc.result = result
assoc.save()
job_context['qc_result'] = result
data_file = ComputedFile()
data_file.filename = "multiqc_data.zip" # This is deterministic
data_file.absolute_file_path = os.path.join(job_context["qc_directory"], data_file.filename)
data_file.calculate_sha1()
data_file.calculate_size()
data_file.is_public = True
data_file.result = job_context['qc_result']
data_file.is_smashable = False
data_file.is_qc = True
data_file.save()
job_context['computed_files'].append(data_file)
SampleComputedFileAssociation.objects.get_or_create(
sample=job_context["sample"],
computed_file=data_file)
report_file = ComputedFile()
report_file.filename = "multiqc_report.html" # This is deterministic
report_file.absolute_file_path = os.path.join(job_context["qc_directory"], report_file.filename)
report_file.calculate_sha1()
report_file.calculate_size()
report_file.is_public = True
report_file.is_smashable = False
report_file.is_qc = True
report_file.result = job_context['qc_result']
report_file.save()
job_context['computed_files'].append(report_file)
job_context['qc_files'] = [data_file, report_file]
return job_context